VEHICLE LICENSE PLATE DETECTION USING YOLO ALGORITHM

Abstract

Urban population growth has created challenges in efficient parking space management. Manual data collection is time-consuming and error-prone, especially at night. Modern technology-based solutions are urgently needed. This research focuses on an innovative parking management system using YOLO for real-time object detection, including license plates. The objective is to assess the YOLO algorithm's accuracy in license plate detection. The methodology follows software development best practices, utilizing Python and Tkinter GUI for an intuitive interface. YOLO and EasyOCR enable object detection and character recognition. Results show high accuracy: 88.8% for HD and 86.3% for sub-HD resolutions. YOLO proves reliable for license plate data collection, reducing manual intervention and enhancing parking management.